Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises: a storage component configured to maintain a set of model control schemes for controlling an industrial process, a control component configured to control the industrial process with a control program running a model control scheme, wherein the model control scheme is configured to optimize a first parameter of the industrial process, and a model management component configured to change the model control scheme to optimize a second parameter of the industrial process that is distinct from the first parameter.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to train machine learning systems to perform autonomous control in an industrial automation environment. In some examples, a data aggregation component receives operational data from Original Equipment Manufacturer (OEM) devices, identifies a device type for the operational data, and transfers the operational data for the device type to a machine learning interface component. The operational data characterizes the operations of the OEM devices. The interface component receives the operational data for the device type and generates feature vectors based on the operational data configured for ingestion by a machine learning model. The interface component transfers the feature vectors to a machine learning model.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component monitors an integrated design application and generates feature vectors that represent operations of the integrated design application. The security component supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the integrated design application. When anomalous behavior is detected in the operations of the integrated design application, the security component generates and transfers an alert that characterizes the anomalous behavior.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to train machine learning systems to perform autonomous control in an industrial automation environment. In some examples, a data aggregation component receives operational data from Original Equipment Manufacturer (OEM) devices, identifies a device type for the operational data, and transfers the operational data for the device type to a machine learning interface component. The operational data characterizes the operations of the OEM devices. The interface component receives the operational data for the device type and generates feature vectors based on the operational data configured for ingestion by a machine learning model. The interface component transfers the feature vectors to a machine learning model.

Abstract: Various embodiments of the present technology generally relate to solutions for integrating machine learning models into industrial automation environments. More specifically, embodiments include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing programs. In an embodiment, a system comprises: a control component configured to run a closed-loop industrial process comprises a first machine learning model; a measurement component configured to measure a gap between outcome data predicted by the first machine learning model and actual outcome data; a determination component configured to determine, based on the gap, that the first machine learning model has degraded; and a management component configured to replace the first machine learning model with a second machine learning model, wherein the second machine learning model is trained based at least in part on the actual outcome data.

Abstract: Various embodiments of the present technology generally relate to integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises a screen capture component configured to capture images of a human-machine interface in an industrial automation environment, wherein the one or more images include at least one visual depiction of data collected from an industrial device. The system further comprises an input component configured to provide the images to a machine learning model configured to analyze an operating condition of the industrial device.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based recommendation engine configured to, an industrial programming environment, generate a recommendation to add a component to control logic based on an existing portion of the control logic. A notification component is configured to surface the recommendation in the programming environment. A programming component is configured to, in the programming environment, add the component to the control logic.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component monitors an integrated design application and generates feature vectors that represent operations of the integrated design application. The security component supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the integrated design application. When anomalous behavior is detected in the operations of the integrated design application, the security component generates and transfers an alert that characterizes the anomalous behavior.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component generates feature vectors that represents inputs and outputs to a data pipeline and supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the data pipeline. When anomalous behavior is detected in the operations of the data pipeline, the security component generates and transfers an alert that characterizes the anomalous behavior.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component generates feature vectors that represent operations of a Programmable Logic Controller (PLC) and supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the PLC. When anomalous behavior is detected in the operations of the PLC, the security component generates and transfers an alert that characterizes the anomalous behavior.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to surface machine learning systems in a design application of an industrial automation environment. In some examples, a design component generates a control program configured for implementation by a Programmable Logic Controller (PLC). The design component receives a user input that selects a program tag that represents a target variable in an industrial automation process. In response to the user selection, the design component identifies one or more machine learning models associated with the target variable and displays the one or more machine learning models. The design component receives a user input that selects one of the one or more machine learning models and responsively integrates another program tag that represents the selected machine learning model into the control program.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to train machine learning systems based on user operations in an industrial automation environment. In some examples, a Human Machine Interface (HMI) component displays a machine learning output indicating a training state of a machine learning model on a user interface and user feedback regarding the training state. A machine learning interface component weights feature vectors based on the user feedback and supplies the weighted feature vectors to the machine learning model. The machine learning interface component receives another machine learning output that indicates an updated training state for the model.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to visualize machine learning model status in an industrial automation environment. In some examples, a machine learning component receives process inputs associated with industrial devices in the industrial automated environment. The machine learning component processes the inputs to generate machine learning outputs and transfers the machine learning outputs to influence one or more functions of the industrial devices. The machine learning component reports operational data characterizing the machine learning outputs. A Human Machine Interface (HMI) component displays a visualization of the machine learning component and receives the operational data from the machine learning component.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to train machine learning systems to perform autonomous control in an industrial automation environment. In some examples, a data aggregation component receives operational data from Original Equipment Manufacturer (OEM) devices, identifies a device type for the operational data, and transfers the operational data for the device type to a machine learning interface component. The operational data characterizes the operations of the OEM devices. The interface component receives the operational data for the device type and generates feature vectors based on the operational data configured for ingestion by a machine learning model. The interface component transfers the feature vectors to a machine learning model.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to surface data pipelines in an industrial automation environment. In some examples, a design component generates a control program configured for implementation by a programmable logic controller to control an industrial process. The design component adds program tags to the control program and implements the control program through the programmable logic controller. The design component establishes data pipelines that correspond to the program tags in the control program between data sources associated with the program tags and a machine learning system that consumes process data generated by the data sources. A pipeline management component generates a pipeline suggestion that indicates individual ones of the data pipelines and their corresponding program tags.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods for training a machine learning model for implementation in the environment. In some embodiments, an application receives a data set that comprises a control program configured for implementation by a Programmable Logic Controller (PLC). The application processes the data set and calculates derivative values based on the data set. The design application identifies types for individual ones of the feature vectors and ranks the feature vectors based on their types. The design application weights the feature vectors based on their ranks. The design application generates feature vectors that comprise the derivative values and supplies the weighted feature vectors to the machine learning model for training. The application receives a machine learning training output generated by processing the weighted feature vectors.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to optimize a target variable in an industrial automation environment. In some examples, a design application generates a control program configured and selects a program tag that represents a target variable in an industrial process. A processing application identifies a set of available program tags that represent independent variables in the industrial process and determines correlations between ones of the independent variables and the target variable. The processing application selects available program tags that represent independent variables correlated with the target variable and generates a recommendation that indicates the selected available program tags. The design application modifies the control program using the selected available program tags to optimize the target variable.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods for applying machine learning techniques to industrial control code to detect errors, perform optimizations, and generate predictions. In some embodiments, a design application in an industrial automation environment generates a functional block diagram configured for implementation by a programmable logic controller. The design application generates feature vectors that represent the functional block diagram configured for ingestion by a machine learning model. The design application supplies the feature vectors to the machine learning model. The design application receives a machine learning output that comprises the recommendation feedback generated by the machine learning model and responsively modifies the functional block diagram based on the recommendation feedback.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments of the present technology include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a user interface component configured to display a programming environment for editing control logic, wherein operational data from the industrial automation environment is accessible from within the programming environment through a data pipeline. A machine learning-based data science engine is configured to process the operational data from the industrial automation environment to generate processed data and identify a portion of the processed data relevant to a component of the control logic.